UNASSIGNED: Libraries provide access to databases with auto-cite features embedded into the services; however, the accuracy of these auto-cite buttons is not very high in humanities and social sciences databases.
UNASSIGNED: This case compares two biomedical databases, Ovid MEDLINE and PubMed, to see if either is reliable enough to confidently recommend to students for use when writing papers. A total of 60 citations were assessed, 30 citations from each citation generator, based on the top 30 articles in PubMed from 2010 to 2020.
UNASSIGNED: Error rates were higher in Ovid MEDLINE than PubMed but neither database platform provided error-free references. The auto-cite tools were not reliable. Zero of the 60 citations examined were 100% correct. Librarians should continue to advise students not to rely solely upon citation generators in these biomedical databases.

OBJECTIVE: Author name incompleteness, referring to only first initial available instead of full first name, is a long-standing problem in MEDLINE and has a negative impact on biomedical literature systems. The purpose of this study is to create an Enhanced Author Names (EAN) dataset for MEDLINE that maximizes the number of complete author names.
METHODS: The EAN dataset is built based on a large-scale name comparison and restoration with author names collected from multiple literature databases such as MEDLINE, Microsoft Academic Graph, and Semantic Scholar. We assess the impact of EAN on biomedical literature systems by conducting comparative and statistical analyses between EAN and MEDLINE\'s author names dataset (MAN) on 2 important tasks, author name search and author name disambiguation.
RESULTS: Evaluation results show that EAN improves the number of full author names in MEDLINE from 69.73 million to 110.9 million. EAN not only restores a substantial number of abbreviated names prior to the year 2002 when the NLM changed its author name indexing policy but also improves the availability of full author names in articles published afterward. The evaluation of the author name search and author name disambiguation tasks reveal that EAN is able to significantly enhance both tasks compared to MAN.
CONCLUSIONS: The extensive coverage of full names in EAN suggests that the name incompleteness issue can be largely mitigated. This has significant implications for the development of an improved biomedical literature system. EAN is available at https://zenodo.org/record/10251358, and an updated version is available at https://zenodo.org/records/10663234.

Surgical site infections (SSIs) pose a significant clinical challenge, with heightened risks and severe consequences for diabetic patients undergoing surgical procedures. This systematic review aims to synthesize the current evidence on effective prevention strategies for mitigating SSI risk in this vulnerable population. From inception to March 2024, we comprehensively searched multiple electronic databases (PubMed, Medline, Embase, Cochrane Library, CINAHL) to identify relevant studies evaluating SSI prevention strategies in diabetic surgical patients. Our search strategy followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, utilizing a combination of keywords and Medical Subject Headings (MeSH) terms related to diabetes, surgical site infections, prevention strategies, and surgical procedures. Inclusion criteria focused on peer-reviewed clinical trials, randomized controlled trials, and meta-analyses published in English. The search yielded three studies meeting the eligibility criteria, subject to data extraction and qualitative synthesis. Key findings highlighted the efficacy of interventions such as optimized perioperative glycemic control, timely prophylactic antibiotic administration, and meticulous preoperative skin antisepsis in reducing SSI rates among diabetic surgical patients. The potential for personalized prevention approaches based on individual patient factors, such as diabetes type and surgical complexity, was explored. This systematic review underscores the importance of a multifaceted, evidence-based approach to SSI prevention in diabetic surgical patients, integrating strategies like glycemic control, antibiotic prophylaxis, and preoperative skin antisepsis. Furthermore, our findings suggest the potential benefits of personalized care pathways tailored to individual patient characteristics. Implementing these interventions requires interdisciplinary collaboration, adaptation to diverse healthcare settings, and patient engagement through culturally sensitive education initiatives. This comprehensive analysis informs clinical practice, fosters patient safety, and contributes to the global efforts to enhance surgical outcomes for this high-risk population.

BACKGROUND: Traditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing.
RESULTS: Word embeddings, which represent a word\'s context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug\'s repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE.
CONCLUSIONS: The annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique\'s suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

BACKGROUND: Palliative care in low- or middle-income country (LMIC) humanitarian settings is a new area, experiencing a degree of increased momentum over recent years. The review contributes to this growing body of knowledge, in addition to identifying gaps for future research. The overall aim is to systematically explore the evidence on palliative care needs of patients and/or their families in LMIC humanitarian settings.
METHODS: Arksey and O\'Malley\'s (Int J Soc Res Methodol. 8:19-32, 2005) scoping review framework forms the basis of the study design, following further guidance from Levac et al. (Implement Sci 5:1-9, 2010), the Joanna Briggs Institute (JBI) Peters et al. (JBI Reviewer\'s Manual JBI: 406-452, 2020), and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) from Tricco et al. (Ann Intern Med 169:467-73, 2018). This incorporates a five-step approach and the population, concept, and context (PCC) framework. Using already identified key words/terms, searches for both published research and gray literature from January 2012 to October 2022 will be undertaken using databases (likely to include Cumulative Index of Nursing and Allied Health (CINAHL), MEDLINE, Embase, Global Health, Scopus, Applied Social Science Index and Abstracts (ASSIA), Web of Science, Policy Commons, JSTOR, Library Network International Monetary Fund and World Bank, Google Advanced Search, and Google Scholar) in addition to selected pre-print sites and websites. Data selection will be undertaken based on the inclusion and exclusion criteria and will be reviewed at each stage by two reviewers, with a third to resolve any differences. Extracted data will be charted in a table. Ethical approval is not required for this review.
CONCLUSIONS: Findings will be presented in tables and diagrams/charts, followed by a narrative description. The review will run from late October 2022 to early 2023. This is the first systematic scoping review specifically exploring the palliative care needs of patients and/or their family, in LMIC humanitarian settings. The paper from the review findings will be submitted for publication in 2023.

BACKGROUND: Deep brain stimulation (DBS) can be used to treat several neurological and psychiatric conditions such as Parkinson\'s disease, epilepsy and obsessive-compulsive disorder; however, limited work has been done to assess the disparities in DBS access and implementation. The goal of this scoping review is to identify sources of disparity in the clinical provision of DBS.
METHODS: A scoping review will be conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-extension for Scoping Reviews methodology. Relevant studies will be identified from databases including MEDLINE/PubMed, EMBASE and Web of Science, as well as reference lists from retained articles. Initial search dates were in January 2023, with the study still ongoing. An initial screening of the titles and abstracts of potentially eligible studies will be completed, with relevant studies collected for full-text review. The principal investigators and coauthors will then independently review all full-text articles meeting the inclusion criteria. Data will be extracted and collected in table format. Finally, results will be synthesised in a table and narrative report.
BACKGROUND: No institutional board review or approval is necessary for the proposed scoping review. The findings will be submitted for publication to relevant peer-reviewed journals and conferences.
UNASSIGNED: This protocol has been registered prospectively on the Open Science Framework (https://osf.io/cxvhu).

UNASSIGNED: Literature searches are routinely used by researchers for conducting systematic reviews as well as by healthcare providers, and sometimes patients, to quickly guide their clinical decisions. Using more than one database is generally recommended but may not always be necessary for some fields. This study aimed to determine the added value of searching additional databases beyond MEDLINE when conducting a literature search of hyperbaric oxygen treatment (HBOT) randomised controlled trials (RCTs).
UNASSIGNED: This study consisted of two phases: a scoping review of all RCTs in the field of HBOT, followed by a a statistical analysis of sensitivity, precision, \'number needed to read\' (NNR) and \'number unique\' included by individual biomedical databases. MEDLINE, Embase, Cochrane Central Register of Control Trials (CENTRAL), and Cumulated Index to Nursing and Allied Health Literature (CINAHL) were searched without date or language restrictions up to December 31, 2022. Screening and data extraction were conducted in duplicate by pairs of independent reviewers. RCTs were included if they involved human subjects and HBOT was offered either on its own or in combination with other treatments.
UNASSIGNED: Out of 5,840 different citations identified, 367 were included for analysis. CENTRAL was the most sensitive (87.2%) and had the most unique references (7.1%). MEDLINE had the highest precision (23.8%) and optimal NNR (four). Among included references, 14.2% were unique to a single database.
UNASSIGNED: Systematic reviews of RCTs in HBOT should always utilise multiple databases, which at minimum include MEDLINE, Embase, CENTRAL and CINAHL.

UNASSIGNED: Studies on the impact of long COVID on work capacity are increasing but are difficult to locate in bibliographic databases, due to the heterogeneity of the terms used to describe this new condition and its consequences. This study aims to report on the effectiveness of different search strategies to find studies on the impact of long COVID on work participation in PubMed and to create validated search strings.
UNASSIGNED: We searched PubMed for articles published on Long COVID and including information about work. Relevant articles were identified and their reference lists were screened. Occupational health journals were manually scanned to identify articles that could have been missed. A total of 885 articles potentially relevant were collected and 120 were finally included in a gold standard database. Recall, Precision, and Number Needed to Read (NNR) of various keywords or combinations of keywords were assessed.
UNASSIGNED: Overall, 123 search-words alone or in combination were tested. The highest Recalls with a single MeSH term or textword were 23 and 90%, respectively. Two different search strings were developed, one optimizing Recall while keeping Precision acceptable (Recall 98.3%, Precision 15.9%, NNR 6.3) and one optimizing Precision while keeping Recall acceptable (Recall 90.8%, Precision 26.1%, NNR 3.8).
UNASSIGNED: No single MeSH term allows to find all relevant studies on the impact of long COVID on work ability in PubMed. The use of various MeSH and non-MeSH terms in combination is required to recover such studies without being overwhelmed by irrelevant articles.

OBJECTIVE: Large language models (LLMs) like OpenAI\'s ChatGPT are powerful generative systems that rapidly synthesize natural language responses. Research on LLMs has revealed their potential and pitfalls, especially in clinical settings. However, the evolving landscape of LLM research in medicine has left several gaps regarding their evaluation, application, and evidence base.
OBJECTIVE: This scoping review aims to (1) summarize current research evidence on the accuracy and efficacy of LLMs in medical applications, (2) discuss the ethical, legal, logistical, and socioeconomic implications of LLM use in clinical settings, (3) explore barriers and facilitators to LLM implementation in healthcare, (4) propose a standardized evaluation framework for assessing LLMs\' clinical utility, and (5) identify evidence gaps and propose future research directions for LLMs in clinical applications.
METHODS: We screened 4,036 records from MEDLINE, EMBASE, CINAHL, medRxiv, bioRxiv, and arXiv from January 2023 (inception of the search) to June 26, 2023 for English-language papers and analyzed findings from 55 worldwide studies. Quality of evidence was reported based on the Oxford Centre for Evidence-based Medicine recommendations.
RESULTS: Our results demonstrate that LLMs show promise in compiling patient notes, assisting patients in navigating the healthcare system, and to some extent, supporting clinical decision-making when combined with human oversight. However, their utilization is limited by biases in training data that may harm patients, the generation of inaccurate but convincing information, and ethical, legal, socioeconomic, and privacy concerns. We also identified a lack of standardized methods for evaluating LLMs\' effectiveness and feasibility.
CONCLUSIONS: This review thus highlights potential future directions and questions to address these limitations and to further explore LLMs\' potential in enhancing healthcare delivery.
CONCLUSIONS: Question What is the current state of Large Language Models’ (LLMs) application in clinical settings, and what are the primary challenges and opportunities associated with their integration? Findings This scoping review, analyzing 55 studies, indicates that while LLMs, including OpenAI’s ChatGPT, show potential in compiling patient notes, aiding in healthcare navigation, and supporting clinical decision-making, their use is constrained by data biases, the generation of plausible but incorrect information, and various ethical and privacy concerns. A significant variability in the rigor of studies, especially in evaluating LLM responses, calls for standardized evaluation methods, including established metrics like ROUGE, METEOR, G-Eval, and MultiMedQA. Meaning The findings suggest a need for enhanced methodologies in LLM research, stressing the importance of integrating real patient data and considering social determinants of health, to improve the applicability and safety of LLMs in clinical environments.

BACKGROUND: Community-based participatory research (CBPR) is a collaborative research approach that equally engages researchers and community stakeholders throughout all steps of the research process to facilitate social change and increase research relevance. Community advisory boards (CABs) are a CBPR tool in which individuals with lived experience and community organisations are integrated into the research process and ensure the work aligns with community priorities. We seek to (1) explore the best practices for the recruitment and engagement of people with lived experiences on CABs and (2) identify the scope of literature on minimising power dynamics between organisations and community members with lived experience who work on CABs together.
METHODS: This scoping review will follow the Arksey and O\'Malley methodological framework, informed by Levac et al, and will be reported using a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram. Detailed and robust search strategies have been developed for Embase, Medline and PsychINFO. Grey literature references and reference lists of included articles published between 1 January 1990 and 30 March 2023 will be considered. Two reviewers will independently screen references in two successive stages of title/abstract and full-text screening. Conflicts will be decided by consensus or a third reviewer. Thematic analysis will be applied in three phases: open coding, axial coding and abstraction. Extracted data will be recorded and presented in a tabular format and/or graphical summaries, with a descriptive overview discussing how the research findings relate to the research questions. At this time, a preliminary search of peer-reviewed and grey literature has been conducted. Search results for peer-reviewed literature have been uploaded to Covidence for review and appraisal for relevance.
BACKGROUND: Formal ethics approval is not required for this review. Review findings will inform ongoing and future CBPR community advisory board dynamics.
BACKGROUND: The protocol has been registered prospectively on the Open Science Framework (https://doi.org/10.17605/OSF.IO/QF5D3).